CN112598723A - Method and device for identifying thickness of stainless steel coated steel bar and storage medium - Google Patents

Method and device for identifying thickness of stainless steel coated steel bar and storage medium Download PDF

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CN112598723A
CN112598723A CN202011495555.XA CN202011495555A CN112598723A CN 112598723 A CN112598723 A CN 112598723A CN 202011495555 A CN202011495555 A CN 202011495555A CN 112598723 A CN112598723 A CN 112598723A
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steel bar
detected
composite steel
face
thickness
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CN112598723B (en
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谭建平
李臻
杨政
张清芳
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Central South University
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Abstract

The invention discloses a method and a device for identifying the thickness of a stainless steel coated steel bar and a storage medium, wherein the method comprises the following steps: acquiring an end face image of the composite steel bar to be detected after sample preparation; rotating the end face image of the composite steel bar to be detected to be horizontal; preprocessing the horizontal composite steel bar end face image to be detected to obtain a binary image of the composite steel bar end face to be detected; extracting the inner and outer side contours of the coating according to the binary image of the end face of the composite steel bar to be detected, and performing pixel calibration; and (3) making N straight lines through the circle center of the composite steel bar to be detected, solving the crossed pixel point position of the inner and outer side profiles of the coating and the N straight lines, and calculating to obtain the minimum value of the coating thickness of the composite steel bar to be detected based on pixel calibration. The mathematical model is simple and reliable, and the identification precision can reach 1 um; the thickness detection of composite steel bars with different diameters can be realized, the robustness is high, and the adaptability is strong; the non-contact detection does not damage the original coating structure and has high detection efficiency.

Description

Method and device for identifying thickness of stainless steel coated steel bar and storage medium
Technical Field
The invention relates to the field of coating thickness detection, in particular to a method and a device for identifying the thickness of a stainless steel coating reinforcing steel bar and a storage medium.
Background
The reinforced concrete structure is the most widely used building structure, but the corrosion resistance of common steel bars is poor, the steel bars are easy to rust and expand, and when the internal stress exceeds the elastic limit of the concrete due to the increase of the volume, the concrete structure can be damaged. According to incomplete statistics, economic loss caused by chloride corrosion in 2019 of China can account for 3% of GDP, wherein corrosion loss related to steel bar corrosion accounts for about 40% of total corrosion loss.
To solve the increasingly serious corrosion problem, the team develops a stainless steel composite steel bar, namely, a steel bar with a core made of carbon steel and a coating made of stainless steel and with excellent performance is obtained by utilizing a processing technology to enable two parts of metals to achieve metallurgical bonding, and the stainless steel composite steel bar is obtained through multiple tests: the corrosion resistance of the composite steel bar is closely related to the thickness of a stainless steel coating, particularly, the forming positions of longitudinal ribs and transverse ribs of the steel bar are more sensitive to corrosion, and the steel requirements in the fields of new generation of electric power, energy, ocean engineering and the like can be met only when the thickness of the coating reaches 0.17 mm. However, at present, there is a lack of an apparatus and method for accurately detecting the thickness of the coating layer, which is also one of the important reasons for limiting the wide application of the coated steel bar.
Patent numbers: CN201921320495.0 has devised a cladding thickness measuring instrument, which is to place a detection film on a substrate and then press the detection film on the substrate through a contact to measure the thickness of the detection film, but the above-mentioned instrument can only measure the whole thickness of the detection film, and cannot realize the independent measurement of the cladding thickness. Patent numbers: CN201921885019.3 has designed a hot galvanizing layer thickness detection device, and its theory of operation is: firstly, a measuring probe of a thickness detector is fixed at the bottom of a pressing plate, then the pressing plate is pushed through an air cylinder, and finally the thickness of a zinc coating is measured by using the measuring probe at the bottom of the pressing plate. The device has two defects, namely, only the galvanized thickness of the plate can be measured, and the coating thickness of the composite section cannot be measured; secondly, the measurement is a contact measurement, which can cause damage to the coating to a certain extent. Patent numbers: CN201510499928.3 designs a transmission line icing thickness recognition method of a genetic wavelet neural network, the method comprises the steps of firstly obtaining an ice coating image of a transmission line through an image sensor, then carrying out preprocessing and edge detection, and finally recognizing the ice coating thickness of the transmission line by using the genetic wavelet neural network, wherein the method has the defects that: firstly, the precision is low and cannot reach the um level; secondly, only the average thickness of the ice coating can be identified, and the maximum value and the minimum value of the thickness of the coating cannot be solved.
In summary, the above detection methods cannot effectively identify the coating thickness of the composite steel bar, and the effective identification of the coating thickness of the steel bar has become a significant technical difficulty limiting the mass production of the steel bar. Therefore, at present, a steel bar coating thickness identification method with high identification precision and non-contact detection is urgently needed to be designed.
Disclosure of Invention
The invention provides a method and a device for identifying the thickness of a stainless steel coated reinforcing steel bar and a storage medium, which are used for solving the problem that the coating thickness of a composite reinforcing steel bar cannot be effectively identified by the existing detection method.
In a first aspect, a method for identifying the thickness of a stainless steel clad steel bar is provided, which comprises the following steps:
acquiring an end face image of the composite steel bar to be detected after sample preparation;
rotating the end face image of the composite steel bar to be detected to be horizontal;
preprocessing the horizontal composite steel bar end face image to be detected to obtain a binary image of the composite steel bar end face to be detected;
extracting the inner and outer side contours of the coating according to the binary image of the end face of the composite steel bar to be detected, and performing pixel calibration;
making N straight lines through the circle center of the composite steel bar to be detected, solving the position of a pixel point of intersection of the inner and outer side profiles of the coating and the N straight lines, and calculating to obtain the minimum value of the coating thickness of the composite steel bar to be detected based on pixel calibration; wherein N is a preset value.
Further, rotating the image of the end face of the composite steel bar to be detected to the horizontal comprises:
recognizing the longitudinal rib of the steel bar by utilizing a linear detection algorithm based on the end face image of the composite steel bar to be detected, and acquiring two points A on the longitudinal rib of the steel bar1(xA1,yA1) And A2(xA2,yA2)；(xA1,yA1) Is represented by A1(x) of (C)A2,yA2) Is represented by A2The coordinates of (a);
with A1(xA1,yA1) Rotating the longitudinal ribs of the reinforcing steel bar to be horizontal by taking the longitudinal ribs as the circle centers, wherein the rotating angle is
Figure BDA0002842060190000021
And obtaining a horizontal image of the end face of the composite steel bar to be detected.
Further, the preprocessing is carried out on the horizontal composite steel bar end face image to be detected, so as to obtain a binaryzation image of the composite steel bar end face to be detected, and the method comprises the following steps:
carrying out gray processing on the horizontal composite steel bar end face image to be detected;
performing noise reduction treatment on the end face image of the composite steel bar to be detected after graying treatment by adopting a self-adaptive median filtering method; specifically, a window scanning image with a preset size can be used, and the median value of the field is used as the gray value of the pixel at the center point of the window, so that the method not only smiles salt and pepper noise to a certain extent, but also protects the edge characteristics of the image;
and carrying out equal division threshold processing on the composite steel bar end face image to be detected after the noise reduction processing to obtain a binary image of the composite steel bar end face to be detected.
Further, the method for performing the equal-division threshold processing on the end face image of the composite steel bar to be detected after the noise reduction processing includes:
identifying the circle center of the composite steel bar to be detected by using a Hough circle transformation detection algorithm, and equally dividing the end face image of the composite steel bar to be detected after noise reduction into a plurality of fan-shaped areas by taking the circle center as the center;
and carrying out independent self-adaptive threshold processing on each sector area, wherein the self-adaptive threshold processing uses a window with a preset size to scan an image subjected to noise reduction processing, and the average value of gray values in the window is used as the threshold value of the area, so that the threshold processing of the window is completed, and finally the binary image of the end face of the composite steel bar to be detected is obtained.
Further, extracting the inner and outer side contours of the coating according to the binary image of the end face of the composite steel bar to be detected, and performing pixel calibration, comprising:
contour extraction: performing edge detection on the binary image of the end face of the composite steel bar to be detected, and extracting the inner and outer side contours of the coating by using a findContours algorithm;
pixel calibration: extracting two points on the leftmost side and the rightmost side of the binary image of the end face of the composite steel bar to be detected according to the inner and outer side outlines of the coating, wherein the two points are B1(xB1,yB1) And B2(xB2,yB2)；(xB1,yB1) Is represented by B1(x) of (C)B2,yB2) Is represented by B2The coordinates of (a);
calculating the physical size of each pixel point in the binary image of the end face of the composite steel bar to be detected according to the following formula:
Figure BDA0002842060190000031
wherein q represents the physical size of each pixel point in the binary image of the end face of the composite steel bar to be detected, and L represents B1(xB1,yB1) And B2(xB2,yB2) The physical actual length, L, between can be measured using a vernier caliper.
Further, the step of making N straight lines through the center of the composite steel bar to be detected, solving the crossed pixel point positions of the inside and outside profiles of the coating and the N straight lines, and calculating the minimum value of the coating thickness of the composite steel bar to be detected based on pixel calibration includes:
over-detection composite steel bar circle center O1(x1,y1) Making N straight lines, wherein the corresponding straight line equation is as follows:
Figure BDA0002842060190000032
wherein N is 1,2,3.. N,
Figure BDA0002842060190000033
four points where each line intersects the inside and outside contours of the cladding are denoted Cn(xCn,yCn)、Dn(xDn,yDn) And En(xEn,yEn)、Fn(xFn,yFn) Then, calculating the thickness of the coating between the two corresponding points based on the physical size of the pixel points as follows:
Figure BDA0002842060190000034
Figure BDA0002842060190000035
minimum value h of coating thicknessminComprises the following steps: h ismin＝min{h11、h12、h21、h22…hN1、hN2}。
Further, still include:
calculating to obtain one or more of the maximum value, the average value and the standard deviation of the coating thickness of the composite steel bar to be detected;
maximum value h of coating thicknessmaxComprises the following steps: h ismax＝max{h11、h12、h21、h22…hN1、hN2}；
Average value of coating thickness
Figure BDA0002842060190000036
Comprises the following steps:
Figure BDA0002842060190000037
standard deviation h of coating thicknessVariance (variance)Comprises the following steps:
Figure BDA0002842060190000038
further, the method for acquiring the end face image of the composite steel bar to be detected after sample preparation further comprises the following steps:
preparing a sample of the composite steel bar to be detected: firstly, grinding the end face of the composite steel bar to be detected by using 160# abrasive paper, 4000# abrasive paper and a polishing machine, and then corroding by using 4% nitric acid alcohol solution until the core part and the coating layer of the composite steel bar to be detected have obvious color difference.
In a second aspect, there is provided a device for identifying the thickness of a stainless steel coated steel bar, comprising:
the image acquisition module is used for acquiring an end face image of the composite steel bar to be detected after sample preparation;
the image rotation module is used for rotating the end face image of the composite steel bar to be detected to the horizontal;
the preprocessing module is used for preprocessing the horizontal composite steel bar end face image to be detected to obtain a binary image of the composite steel bar end face to be detected;
the contour acquisition and calibration module is used for extracting the contour of the inner side and the outer side of the cladding according to the binary image of the end face of the composite steel bar to be detected and carrying out pixel calibration;
the coating thickness obtaining module is used for making N straight lines through the circle center of the composite steel bar to be detected, solving the position of pixel points of intersection of the inner and outer side profiles of the coating and the N straight lines, and calculating to obtain the minimum value of the coating thickness of the composite steel bar to be detected based on pixel calibration; wherein N is a preset value.
In a third aspect, a computer-readable storage medium is provided, which stores a computer program that, when loaded by a processor, performs the method for identifying the thickness of a stainless steel clad steel bar as described above.
Advantageous effects
The invention provides a method and a device for identifying the thickness of a stainless steel coated steel bar and a storage medium, and has the following advantages:
1) the mathematical model is simple and reliable, and the identification precision can reach 1 um;
2) the scheme can realize the thickness detection of the composite steel bars with different diameters, and has high system robustness and strong adaptability;
3) the scheme is non-contact detection, the original coating structure cannot be damaged, and only 50ms is needed for obtaining the final result from the image and outputting the result through experimental verification; the detection efficiency is greatly improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for identifying the thickness of a stainless steel coated steel bar according to an embodiment of the present invention;
fig. 2 is an image of an end face of a composite steel bar to be detected after sample preparation according to an embodiment of the present invention;
fig. 3 is a schematic view of an image rotation of an end face of a composite steel bar to be detected according to an embodiment of the present invention;
fig. 4 is a binarized image of the end face of the composite steel bar to be detected according to the embodiment of the present invention;
fig. 5 is a schematic diagram of extracting a contour of a composite steel bar to be detected according to an embodiment of the present invention;
fig. 6 is a schematic diagram of pixel calibration of a composite rebar to be detected according to an embodiment of the present invention;
FIG. 7 is a graph illustrating the maximum, average, minimum and standard deviation of the coating thickness calculations provided by an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
As shown in fig. 1, according to an embodiment of the present invention, there is provided a method for identifying a thickness of a stainless steel-clad steel bar, including:
s01: acquiring an image of the end face of the composite steel bar to be detected after sample preparation, as shown in fig. 2; the system can be obtained by collecting through an industrial camera with the aid of an illumination system.
S02: rotating the end face image of the composite steel bar to be detected to be horizontal; the method comprises the following steps:
recognizing the longitudinal rib of the steel bar by utilizing a linear detection algorithm based on the end face image of the composite steel bar to be detected, and acquiring two points A on the longitudinal rib of the steel bar1(xA1,yA1) And A2(xA2,yA2)；(xA1,yA1) Is represented by A1(x) of (C)A2,yA2) Is represented by A2The coordinates of (a);
with A1(xA1,yA1) Rotating the longitudinal ribs of the reinforcing steel bar to be horizontal by taking the longitudinal ribs as the circle centers, wherein the rotating angle is
Figure BDA0002842060190000051
And obtaining a horizontal image of the end face of the composite steel bar to be detected, as shown in fig. 3.
S03: preprocessing the horizontal composite steel bar end face image to be detected to obtain a binary image of the composite steel bar end face to be detected; the method comprises the following steps:
carrying out gray processing on the horizontal composite steel bar end face image to be detected;
performing noise reduction treatment on the end face image of the composite steel bar to be detected after graying treatment by adopting a self-adaptive median filtering method; specifically, a window scanning image with a preset size can be used, the median value of the field is used as the gray value of the pixel at the center point of the window, in the embodiment, the size of the window is 5 × 5, and of course, other values can be selected according to actual needs in other embodiments, and the method not only smiles salt and pepper noise to a certain extent, but also protects the edge characteristics of the image;
equally dividing the end face image of the composite steel bar to be detected after noise reduction treatment into threshold values, wherein the threshold value processing comprises the following steps:
recognizing circle center O of composite steel bar to be detected by using Hough circle transformation detection algorithm1(x1,y1) Then, equally dividing the end face image of the composite steel bar to be detected after noise reduction into a plurality of sector areas by taking the circle center as the center; in the present embodiment, the segment is equally divided into 18 sector areas, and of course, in other embodiments, equally divided into 12, 15, 24, etc. may be selected as needed;
and performing independent adaptive threshold processing on each sector region, wherein the adaptive threshold processing uses a window scanning noise-reduced image with a preset size, the size of the window in the embodiment is 3 × 3, the average value of 9 gray values in the window is used as the threshold value of the region, the threshold processing of the window is further completed, and finally the binary image of the end face of the composite steel bar to be detected is obtained, as shown in fig. 4. Of course, in other embodiments, the window size may be selected according to actual needs.
S04: extracting the inner and outer side contours of the coating according to the binary image of the end face of the composite steel bar to be detected, and performing pixel calibration; the method comprises the following steps:
contour extraction: performing edge detection on the binary image of the end face of the composite steel bar to be detected, and extracting the inner and outer side profiles of the coating by using a findContours algorithm, as shown in FIG. 5;
pixel calibration: extracting two points on the leftmost side and the rightmost side of the binary image of the end face of the composite steel bar to be detected according to the inner and outer side outlines of the coating, wherein the two points are B1(xB1,yB1) And B2(xB2,yB2)；(xB1,yB1) Is represented by B1(x) of (C)B2,yB2) Is represented by B2The coordinates of (a);
calculating the physical size of each pixel point in the binary image of the end face of the composite steel bar to be detected according to the following formula:
Figure BDA0002842060190000061
wherein q represents the physical size of each pixel point in the binary image of the end face of the composite steel bar to be detected, and L represents B1(xB1,yB1) And B2(xB2,yB2) The physical actual length between, L, can be measured with a vernier caliper, and the pixel calibration is shown in fig. 6.
S05: making N straight lines through the circle center of the composite steel bar to be detected, solving the position of a pixel point of intersection of the inner and outer side profiles of the coating and the N straight lines, and calculating to obtain the minimum value of the coating thickness of the composite steel bar to be detected based on pixel calibration; wherein N is a preset value, and in this embodiment, N is 15. The method specifically comprises the following steps:
over-detection composite steel bar circle center O1(x1,y1) Making 15 straight lines, wherein the corresponding straight line equation is as follows:
y＝tan(12°×(n-1))(x-x1)+y1wherein n is 1,2,3.. 15
Four points where each line intersects the inside and outside contours of the cladding are denoted Cn(xCn,yCn)、Dn(xDn,yDn) And En(xEn,yEn)、Fn(xFn,yFn) Then, calculating the thickness of the coating between the two corresponding points based on the physical size of the pixel points as follows:
Figure BDA0002842060190000062
Figure BDA0002842060190000063
minimum value h of coating thicknessminComprises the following steps: h ismin＝min{h11、h12、h21、h22…h151、h152}。
Preferably, this embodiment further includes:
calculating to obtain one or more of the maximum value, the average value and the standard deviation of the coating thickness of the composite steel bar to be detected;
maximum value h of coating thicknessmaxComprises the following steps: h ismax＝max{h11、h12、h21、h22…h151、h152}；
Average value of coating thickness
Figure BDA0002842060190000071
Comprises the following steps:
Figure BDA0002842060190000072
standard deviation h of coating thicknessVariance (variance)Comprises the following steps:
Figure BDA0002842060190000073
after the system calculates the minimum, maximum, average and standard deviation of the coating thickness, the result is automatically saved to the background in the form of a document, as shown in fig. 7.
During implementation, before acquiring the to-be-detected composite steel bar end face image after sample preparation, the method further comprises the following steps:
preparing a sample of the composite steel bar to be detected: firstly, grinding the end face of the composite steel bar to be detected by using 160# abrasive paper, 4000# abrasive paper and a polishing machine, and then corroding by using 4% nitric acid alcohol solution until the core part and the coating layer of the composite steel bar to be detected have obvious color difference.
Another embodiment of the present invention provides a device for identifying a thickness of a stainless steel coated reinforcing bar, including:
the image acquisition module is used for acquiring an end face image of the composite steel bar to be detected after sample preparation;
the image rotation module is used for rotating the end face image of the composite steel bar to be detected to the horizontal;
the preprocessing module is used for preprocessing the horizontal composite steel bar end face image to be detected to obtain a binary image of the composite steel bar end face to be detected;
the contour acquisition and calibration module is used for extracting the contour of the inner side and the outer side of the cladding according to the binary image of the end face of the composite steel bar to be detected and carrying out pixel calibration;
the coating thickness obtaining module is used for making N straight lines through the circle center of the composite steel bar to be detected, solving the position of pixel points of intersection of the inner and outer side profiles of the coating and the N straight lines, and calculating to obtain the minimum value of the coating thickness of the composite steel bar to be detected based on pixel calibration; wherein N is a preset value.
An embodiment of the present invention also provides a computer-readable storage medium storing a computer program which, when loaded by a processor, performs the method for identifying the thickness of a stainless steel-clad steel bar as described above.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims (10)

1. A method for identifying the thickness of a stainless steel coated steel bar is characterized by comprising the following steps:
acquiring an end face image of the composite steel bar to be detected after sample preparation;
rotating the end face image of the composite steel bar to be detected to be horizontal;
preprocessing the horizontal composite steel bar end face image to be detected to obtain a binary image of the composite steel bar end face to be detected;
extracting the inner and outer side contours of the coating according to the binary image of the end face of the composite steel bar to be detected, and performing pixel calibration;
making N straight lines through the circle center of the composite steel bar to be detected, solving the position of a pixel point of intersection of the inner and outer side profiles of the coating and the N straight lines, and calculating to obtain the minimum value of the coating thickness of the composite steel bar to be detected based on pixel calibration; wherein N is a preset value.
2. The method for identifying the thickness of the stainless steel clad steel bars according to claim 1, wherein the step of rotating the end face image of the composite steel bar to be detected to be horizontal comprises the following steps:
recognizing the longitudinal rib of the steel bar by utilizing a linear detection algorithm based on the end face image of the composite steel bar to be detected, and acquiring two points A on the longitudinal rib of the steel bar1(xA1,yA1) And A2(xA2,yA2)；
With A1(xA1,yA1) Rotating the longitudinal ribs of the reinforcing steel bar to be horizontal by taking the longitudinal ribs as the circle centers, wherein the rotating angle is
Figure FDA0002842060180000011
And obtaining a horizontal image of the end face of the composite steel bar to be detected.
3. The method for identifying the thickness of the stainless steel coated steel bar according to claim 1, wherein the step of preprocessing the horizontal composite steel bar end face image to be detected to obtain a binary image of the end face of the composite steel bar to be detected comprises the following steps:
carrying out gray processing on the horizontal composite steel bar end face image to be detected;
performing noise reduction treatment on the end face image of the composite steel bar to be detected after graying treatment by adopting a self-adaptive median filtering method;
and carrying out equal division threshold processing on the composite steel bar end face image to be detected after the noise reduction processing to obtain a binary image of the composite steel bar end face to be detected.
4. The method for identifying the thickness of the stainless steel coated steel bar according to claim 3, wherein the step of performing equal-division threshold processing on the end face image of the composite steel bar to be detected after the noise reduction processing comprises the following steps:
identifying the circle center of the composite steel bar to be detected by using a Hough circle transformation detection algorithm, and equally dividing the end face image of the composite steel bar to be detected after noise reduction into a plurality of fan-shaped areas by taking the circle center as the center;
and (4) carrying out independent self-adaptive threshold processing on each sector area to obtain a binary image of the end face of the composite steel bar to be detected.
5. The method for identifying the thickness of the stainless steel coated reinforcing steel bar according to claim 1, wherein the steps of extracting the inner and outer contours of the coating according to the binary image of the end face of the composite reinforcing steel bar to be detected and performing pixel calibration comprise:
contour extraction: performing edge detection on the binary image of the end face of the composite steel bar to be detected, and extracting the inner and outer side contours of the coating by using a findContours algorithm;
pixel calibration: extracting two points on the leftmost side and the rightmost side of the binary image of the end face of the composite steel bar to be detected according to the inner and outer side outlines of the coating, wherein the two points are B1(xB1,yB1) And B2(xB2,yB2)；
Calculating the physical size of each pixel point in the binary image of the end face of the composite steel bar to be detected according to the following formula:
Figure FDA0002842060180000021
wherein q represents the physical size of each pixel point in the binary image of the end face of the composite steel bar to be detected, and L represents B1(xB1,yB1) And B2(xB2,yB2) The physical actual length in between.
6. The method for identifying the thickness of the stainless steel clad steel bar according to claim 5, wherein the step of drawing N straight lines through the center of the composite steel bar to be detected, solving the positions of pixel points of intersection of the inner and outer profiles of the clad layer and the N straight lines, and calculating the minimum value of the clad layer thickness of the composite steel bar to be detected based on pixel calibration comprises the following steps:
over-detection composite steel bar circle center O1(x1,y1) Making N straight lines, wherein the corresponding straight line equation is as follows:
Figure FDA0002842060180000022
wherein N is 1,2,3.. N,
Figure FDA0002842060180000023
four points where each line intersects the inside and outside contours of the cladding are denoted Cn(xCn,yCn)、Dn(xDn,yDn) And En(xEn,yEn)、Fn(xFn,yFn) Then, calculating the thickness of the coating between the two corresponding points based on the physical size of the pixel points as follows:
Figure FDA0002842060180000024
Figure FDA0002842060180000025
minimum value h of coating thicknessminComprises the following steps: h ismin＝min{h11、h12、h21、h22…hN1、hN2}。
7. The method of identifying the thickness of the stainless steel coated reinforcing bar according to claim 6, further comprising:
calculating to obtain one or more of the maximum value, the average value and the standard deviation of the coating thickness of the composite steel bar to be detected;
maximum value h of coating thicknessmaxComprises the following steps: h ismax＝max{h11、h12、h21、h22…hN1、hN2}；
Average value of coating thickness
Figure FDA0002842060180000026
Comprises the following steps:
Figure FDA0002842060180000027
standard deviation h of coating thicknessVariance (variance)Comprises the following steps:
Figure FDA0002842060180000028
8. the method for identifying the thickness of the stainless steel coated steel bars according to any one of claims 1 to 7, wherein before the step of obtaining the end face image of the composite steel bars to be detected after the sample preparation, the method further comprises the following steps:
preparing a sample of the composite steel bar to be detected: firstly, grinding the end face of the composite steel bar to be detected by using 160# abrasive paper, 4000# abrasive paper and a polishing machine, and then corroding by using 4% nitric acid alcohol solution until the core part and the coating layer of the composite steel bar to be detected have obvious color difference.
9. A stainless steel clad steel bar thickness recognition device, comprising:
the image acquisition module is used for acquiring an end face image of the composite steel bar to be detected after sample preparation;
the image rotation module is used for rotating the end face image of the composite steel bar to be detected to the horizontal;
the preprocessing module is used for preprocessing the horizontal composite steel bar end face image to be detected to obtain a binary image of the composite steel bar end face to be detected;
the contour acquisition and calibration module is used for extracting the contour of the inner side and the outer side of the cladding according to the binary image of the end face of the composite steel bar to be detected and carrying out pixel calibration;
the coating thickness obtaining module is used for making N straight lines through the circle center of the composite steel bar to be detected, solving the position of pixel points of intersection of the inner and outer side profiles of the coating and the N straight lines, and calculating to obtain the minimum value of the coating thickness of the composite steel bar to be detected based on pixel calibration; wherein N is a preset value.
10. A computer-readable storage medium storing a computer program, wherein the computer program when loaded by a processor performs the method of identifying a thickness of stainless steel coated steel bars according to any one of claims 1 to 7.
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